Industry cooperation for advanced analysis for future heating
By working together with the industry using advanced analysis, including machine learning methods, the goal is to create decision support and alarms for energy distributors and "prosumers" to correct errors and improve energy efficiency.
The project goal is to gradually improve precision in the identification of deviations and patterns regarding heat and cooling delivery by using advanced analysis, artificial intelligence and machine learning. This is done by strengthening the collaboration between the industry and the academy within data and analysis by:
- Developing common data definitions as well as test and training data for input data and around the deviations, errors and patterns that are of high priority.
- A joint collaboration portal to continuously share test and training data and analysis results.
The purpose is to be able to recommend actions to the energy distributor and prosumer to correct errors and improve energy efficiency. Examples of errors that can be identified include measurement data errors, leaks and control errors. The aim is also to develop analytical models that make recommendations for energy efficiency measures by distributors and procurers to improve the efficiency of the entire energy system, through deeper insight into the energy demand patterns of procumers.